Title
Multilevel component analysis of time-resolved metabolic fingerprinting data
Author
Jansen, J.J.
Hoefsloot, H.C.J.
van der Greef, J.
Timmerman, M.E.
Smilde, A.K.
TNO Kwaliteit van Leven
Publication year
2005
Abstract
Genomics-based technologies in systems biology have gained a lot of popularity in recent years. These technologies generate large amounts of data. To obtain information from this data, multivariate data analysis methods are required. Many of the datasets generated in genomics are multilevel datasets, in which the variation occurs on different levels simultaneously (e.g. variation between organisms and variation in time). We introduce multilevel component analysis (MCA) into the field of metabolic fingerprinting to separate these different types of variation. This is in contrast to the commonly used principal component analysis (PCA) that is not capable of doing this: in a PCA model the different types of variation in a multilevel dataset are confounded. MCA generates different submodels for different types of variation. These submodels are lower-dimensional component models in which the variation is approximated. These models are easier to interpret than the original data. Multilevel simultaneous component analysis (MSCA) is a method within the class of MCA models with increased interpretability, due to the fact that the time-resolved variation of all individuals is expressed in the same subspace. MSCA is applied on a time-resolved metabolomics dataset. This dataset contains 1H NMR spectra of urine collected from 10 monkeys at 29 time-points during 2 months. The MSCA model contains a submodel describing the biorhythms in the urine composition and a submodel describing the variation between the animals. Using MSCA the largest biorhythms in the urine composition and the largest variation between the animals are identified. Comparison of the MSCA model to a PCA model of this data shows that the MSCA model is better interpretable: the MSCA model gives a better view on the different types of variation in the data since they are not confounded. © 2004 Elsevier B.V. All rights reserved.
Subject
Biology
Analytical research
Biorhythms
NMR
Principal component analysis
Types of variation
Urinalysis
Data reduction
Genes
Information analysis
Nuclear magnetic resonance
Principal component analysis
Fingerprinting data
Multilevel component analysis (MCA)
Systems biology
Metabolism
Biological rhythm
Controlled study
Data analysis
Female
Genetic database
Genomics
Intermethod comparison
Male
Metabolomics
Monkey
Multilevel component analysis
Multivariate analysis
Nonhuman
Principal component analysis
Priority journal
Proton nuclear magnetic resonance
Statistical model
Urinalysis
Animalia
To reference this document use:
http://resolver.tudelft.nl/uuid:23dbb7f6-1fa4-4f90-b5f0-70aaf58fa1f3
DOI
https://doi.org/10.1016/j.aca.2004.09.074
TNO identifier
238345
ISSN
0003-2670
Source
Analytica Chimica Acta, 530 (2), 173-183
Document type
article